********************************************************************************
* Margins Table, Nondurables
********************************************************************************
clear
clear matrix
set more off
set scheme s1color
estimates clear
graph drop _all
set matsize 2500
log close _all

*Set Directory
cd "../Do"

********************************************************************************
** LOAD AND CLEAN DATA
********************************************************************************

use ../Data/matched_data_nondurables_jun2018_baseline.dta, clear

*list of spending categories that will add up to nondurables (first version excludes only sports, which is excluded because it has durable goods in it)
#delimit ;
local spendcats "
electricity water heatingfuel phonecable housecleaningproducts housecleaningservice
gardenproducts gardenservice clothing personalcare drugs healthcareservices medsupplies entertainment
hobbies personalservices otherchildspending foodhome foodout gasoline";
#delimit cr

*based on content of "spendcats" above, nondurables defined as all included in that group except sports
egen nondurables = rowtotal(`spendcats')

*not that nondurables has one outlier value over 29000 (for monthly spending on nondurable goods/services) 
*before running some regressions, extreme values for nondurables will be omitted; a small number of zeroes will be dropped

drop ethnicity

recode mort (5=0)
recode stocks (5=0)
recode retacct (5=0)
recode howner (5=0)

* Recode some expectations variables from the Inflation surveys. 

* unemployment dummies
recode q6 (1=1) (2 3 = 0), gen(unemp_increase)
recode q6 (3=1) (1 2 = 0), gen(unemp_decrease)

gen conditions_12m = q2a
* note everybody who says "other" is in separate category
recode q2a (1=1) (2 3 = 0), gen(conditions_12m_better)
recode q2a (2=1) (1 3 = 0), gen(conditions_12m_worse)

gen interestrate_12m = q7
recode interestrate_12m (1=1) (2 3 = 0), gen(intrate_12m_up)
recode interestrate_12m (3=1) (1 2 = 0), gen(intrate_12m_down)

gen bconditions_12m = q4
* note everybody who says "other" is in separate category
recode q4 (1=1) (2 3 = 0), gen(bconditions_12m_better)
recode q4 (2=1) (1 3 = 0), gen(bconditions_12m_worse)

* house price forecasts
gen hppoint = .
replace hppoint = 0 if q41==3
replace hppoint = q42 if q41==1
replace hppoint = -q42 if q41==2
* there are some extreme outliers (in the tens of thousands)
replace hppoint = . if abs(hppoint)>=200
la var hppoint "House price expectation"

* Deflation: using 2012q1 PCUN (nondurables CPI): generate a new variable so regressions can be run on nominal and real data alternatively
* from Arman: note that PCUN was redefined in the haver do file to give 2012q1 dollars

rename spend_month date_monthly
sort date_monthly
merge m:1 date_monthly using "../Data/haver_m.dta"

tab _merge
drop if _merge==2
drop _merge

*MB: my simple deflation command: just deflating my main measure of nondurables
gen nondurables_real=nondurables/PCUN
rename date_monthly spend_month
*note real values are only a little higher on average than nominal values

*** Prepare some additional descriptives
* Race isn't reported in all periods. 
preserve
collapse (mean) race , by(prim_key)
sort prim_key
tempfile race
save `race'
restore

drop race
sort prim_key
merge m:1 prim_key using `race'
tab _merge
drop _merge
drop if prim_key==""

recode race (1=1) (nonmissing = 0), gen(white)
gen nonwhite = 1-white
recode gender (2=1) (1=0), gen(female)
recode highesteducation (4 9 = 0) (10/16 = 1), gen(coll)

* q31s* gives codes. Kind of odd that they've created separate variables. 
*MB: odd to generate employed variable, because everyone in sample is employed--however some also say they are retired (?) 
gen employed = . 
replace employed=1 if q31s1==1
replace employed = 0 if (q31s2==2 | q31s3==3 | q31s4==4 | q31s5==5 | q31s6==6 | q31s7==7) & q31s1!=1


* generate currently retired variable.
*odd that some are retired even though all are employed; how do retired people have wage growth expectations? they may be working a small job anyway
drop retired 
gen retired = . 
replace retired = 1 if q31s5==5
replace retired = 0 if (q31s1==1 | q31s2==2 | q31s3==3 | q31s4==4 | q31s6==6 | q31s7==7) & q31s5!=5
tab retired

*more preparation for regressions
local expectations "intrate_12m_up intrate_12m_down unemp_increase unemp_decrease rw_expect d_wageiqr hppoint"
local infl1 "d_inflmedian d_infliqr"
local infl2 "d_longinflmedian d_longinfliqr"

*MBwhen using lagss--may need to rename lagged vars or these locals to match
*local lags1 "lagE_d_inflmedian lagE_d_infliqr" ;
*local lags2 "lagE_d_longinflmedian lagE_d_longinfliqr" ;

local spec1 "Short-Run Infl. Exp."
local spec2 "Medium-Run Infl. Exp."
la var d_inflmedian "Inflation Expectations"
la var d_infliqr "Inflation Uncertainty"

*replace howner=0 if howner==.
*adds ten people to homeowner status; there are still some with howner_fix==0 who have positive mortgage payment, but missing data for has mortgage and/or mortgage amount
*two observations have howner==0 and mort==1 (say they're not a homeowner but they say they have a mortgage); not important so not recoding them for now

*the mortgage dummy is non-missing for over 2000 observations; however the mortgage balance (amtmort) is observed only for 990 people
*tab mort
*reasonable recode of mortgage dummy based on other information
replace mort = 0 if (howner!=1 | mortgage==0) & mort==.
replace mort = 1 if (howner==1 | (mortgage>0 & mortgage!=.)) & mort==.
la var mort "Mortgage Indicator"

*MB: generating quasi-continuous income variable based on midpoint of ranges of annual household income variables (familyincome and familyincome_part2)
rename familyincome inc2
rename familyincome_part2 inc2_2
drop if inc2==.
*here is the income variable: "new_faminc"
gen new_faminc=.
replace new_faminc=2500 if inc2==1
replace new_faminc=6250 if inc2==2
replace new_faminc=8750 if inc2==3
replace new_faminc=11250 if inc2==4
replace new_faminc=13750 if inc2==5
replace new_faminc=17500 if inc2==6
replace new_faminc=22500 if inc2==7
replace new_faminc=27500 if inc2==8
replace new_faminc=32500 if inc2==9
replace new_faminc=37500 if inc2==10
replace new_faminc=45000 if inc2==11
replace new_faminc=55000 if inc2==12
replace new_faminc=67500 if inc2==13
replace new_faminc=87500 if inc2==14 & (inc2_2==1 | inc2_2==.)
*above accounts for one person with inc2==14 and inc2_2 missing; not sure why that's the case but I asigned them the lowest category of income over $75000
replace new_faminc=112500 if inc2==14 & inc2_2==2
replace new_faminc=162500 if inc2==14 & inc2_2==3
replace new_faminc=237500 if inc2==14 & inc2_2==4

gen log_new_faminc=log(new_faminc)


sort prim_key spend_month
*bringing in SAMPLE WEIGHTS, to monthly data: warning, we might lose observations if the set requiring weights has changed ; this will affect our ability to run regs using non-employed types (can only do unweighted) 
merge m:1 prim_key using ../Data/mweights_pooled
*these are the reg weights, the full sample weights will still be called weight_full
*weight variable is called "weight_samp" to indicate the weights were designed for the regression sample
*195 observations dropped that didn't merge with a weight_samp
drop if _merge!=3
drop _merge

*define regression sample before recentering any variables
*drop people with extreme values for spending, inflation expectations, mortgage payment that looks like a mortgage balance
*outliers identified by Ali: turn on as alternative MB: the below prim_key doesn't look suspicious to me---this drop may have been related to the previous (bad) income variable
*drop if prim_key=="5041140:1"
*drops 
drop if mortgage>200000 & mortgage!=.
*drops 17 observations
drop if d_inflmedian>35
*drops 13 observations
drop if nondurables==0
*drops 1 observation
drop if nondurables>28000

*assigning locals for weights (can turn on or off in regression)
local weights "[pweight=weight_samp]"
local weights_full "[pweight=weight_full]"
local pwfile "_pw"
*drop those with missing values for regressors
la var d_inflmedian "Inflation Expectation"
la var d_infliqr "Inflation Uncertainty"
la var d_longinflmedian "Inflation Expectation"
la var d_longinfliqr "Inflation Uncertainty"
la var lag_IE "Lagged Inflation Expectation"
la var lag_infl_iqr "Lagged Infl. Uncertainty"

*variable for sum of monthly payments--interactions between this variable and IE may be included in some models
gen payments=mortgage+car if howner==1
replace payments=rent+car if howner!=1
gen log_payments=.
replace log_payments=log(payments) if payments>0
replace log_payments=0 if payments==0
la var log_payments "Monthly Payments (Log)"
*drop one observation with extreme value for payments (110,000): only if running a regression interacting with payments
drop if payments>100000


*defining sample; removing those with missing values
drop if weight_samp==.
drop if d_inflmedian==.
drop if d_infliqr==.
drop if nondurables==.
*dropping lagged IE: only need if we include lag IE in regression 
drop if lag_IE==.
drop if lag_infl_iqr==.
*new income variable: new_faminc is recode of categorical variables familyincome and familyincome_part2; former variable was earnings last month and highly unreliable
drop if new_faminc==.
drop if intrate_12m_up==.
drop if intrate_12m_down==.
drop if unemp_increase==.
drop if unemp_decrease==.
drop if rw_expect==.
drop if d_wageiqr==.
drop if rage==.
drop if nonwhite==.
drop if female==.
drop if coll==.
drop if retired==.
drop if mort==.
*alternative: comment out the last two drops and omit howner and hppoint from regressions 
*below results in loss of 300+ observations--will retain alternative and omit hppoint from the regression
drop if hppoint==.
*this drops 111 observations--can run alternative model without howner dummy and include these 
drop if howner==.

*replacing inflation expectations (and lags) with centered version: note that means are not weighted; this doesn't matter because weighted mean is within 0.04 ppts of unweighted mean IE
egen IE_sampmean=mean(d_inflmedian) 
replace d_inflmedian=d_inflmedian-IE_sampmean
egen lag_IE_sampmean=mean(lag_IE)
replace lag_IE=lag_IE-lag_IE_sampmean
egen lag_unc_sampmean=mean(lag_infl_iqr)
replace lag_infl_iqr=lag_infl_iqr-lag_unc_sampmean

*replacing log monthly payments with centered version
egen log_payments_sampmean=mean(log_payments)
replace log_payments=log_payments-log_payments_sampmean
*replacing log household income with centered version
egen log_inc_sampmean=mean(log_new_faminc)
replace log_new_faminc=log_new_faminc-log_inc_sampmean

sort prim_key spend_month

*need to xtset the data
destring prim_key, generate(id_new) ignore(":")
xtset id_new spend_month

*generate within-person means of time-varying independent variables: note that for variables that were recentered above, these now represent the within-household average of the deviation from the sample mean for the given variable
egen IE_bar=mean(d_inflmedian), by(id_new)
egen IE_unc_bar=mean(d_infliqr), by(id_new)
egen new_faminc_bar=mean(new_faminc), by(id_new)
egen log_new_faminc_bar=mean(log_new_faminc), by(id_new)
egen intrate_up_bar=mean(intrate_12m_up), by(id_new)
egen intrate_down_bar=mean(intrate_12m_down), by(id_new)
egen unemp_up_bar=mean(unemp_increase), by(id_new)
egen unemp_down_bar=mean(unemp_decrease), by(id_new)
egen rw_bar=mean(rw_expect), by(id_new)
egen rw_unc_bar=mean(d_wageiqr), by(id_new)
egen hp_bar=mean(hppoint), by(id_new)
egen howner_bar=mean(howner), by(id_new)
egen mort_bar=mean(mort), by(id_new)
egen log_payments_bar=mean(log_payments), by(id_new)
** Within-person mean of lagged IE and lagged infl uncertainty (new)
*egen lag_IE_bar=mean(lagE_d_inflmedian), by(id_new)
*egen lag_IE_unc_bar=mean(lagE_d_infliqr), by(id_new)

** Adjusting vars for labelling problems (?) Something weird going on..
gen mort2 = mort
la var mort2 "Has Mortgage"
gen howner2 = howner
la var howner2 "Homeowner"
drop mort howner
ren mort2 mort
ren howner2 howner

** Reverse College
gen nocoll = 1-coll

** Reverse Mortgage
gen nomort = 1-mort
gen nomort_bar = 1-mort_bar

** Label variables for tables
la var log_new_faminc "Household Income (Log)"
la var log_new_faminc_bar "Mean Household Income (within-subject)"
la var IE_bar "Mean Inflation Expectation (within-subject)"
la var IE_unc_bar "Mean Inflation Uncertainty"
la var log_payments_bar "Mean Monthly Payments"
la var unemp_up_bar "Mean Unemp Increase"
la var unemp_down_bar "Mean Unemp Decrease"
la var intrate_up_bar "Mean Int Rates Increase" 
la var intrate_down_bar "Mean Int Rates Decrease"
la var rw_bar "Mean Real Wage Exp."
la var rw_unc_bar "Mean Wage Uncertainty (within-subject)"
la var hp_bar "Mean House Price Exp."
la var hppoint "House Price Expectation"
la var howner_bar "Mean Homeowner"
la var nomort_bar "Mean No Mortgage Indicator"
la var nomort "No Mortgage"
*la var lagE_d_inflmedian "Lagged Infl. Exp."
*la var lagE_d_infliqr "Lagged Infl. Unc."
*la var lag_IE_bar "Mean Lagged Infl. Exp."
*la var lag_IE_unc_bar "Mean Lagged Infl. Unc."
la var nocoll "No College"
la var retired "Retired"
la var rage "Age" 
la var nonwhite "Non-White"
la var rw_expect "Real Wage Expectation"
la var d_wageiqr "Wage Uncertainty"
la var intrate_12m_up "Int Rates Inc"
la var intrate_12m_down "Int Rates Dec"
la var howner "Homeowner"
la var female "Female"
la var unemp_increase "Unemp Inc"
la var unemp_decrease "Unemp Dec"
la var log_payments "Monthly Payments (Log)"

********************************************************************************
** NONDURABLES SAMPLE
********************************************************************************

*generating regression samples
*generate variable that equals 1 in all cases, to sum to determine observations per person
gen pre_obs=1
*generate sum of observations per person
egen obs=total(pre_obs), by(id_new)
*define sample  based on sufficient observation
gen nondurables_sample_hp=(obs>=3)

count if nondurables_sample_hp==1

**REPLICATE WORKING PAPER, Table 6b
#delimit ;
xtgee nondurables_real c.d_inflmedian##i.nocoll c.d_infliqr##i.nocoll log_payments 
c.d_inflmedian##c.log_payments_bar c.d_inflmedian##c.nomort_bar c.d_inflmedian##c.log_new_faminc_bar 
i.unemp_increase i.unemp_decrease i.intrate_12m_up i.intrate_12m_down  
rw_expect d_wageiqr  log_new_faminc  i.nomort i.nocoll rage i.nonwhite i.female i.retired i.howner hppoint
IE_bar IE_unc_bar intrate_up_bar intrate_down_bar 
unemp_up_bar unemp_down_bar rw_bar rw_unc_bar howner_bar hp_bar 
c.lag_IE##i.nocoll c.lag_infl_iqr##i.nocoll 
i.spend_month [pweight=weight_samp] if nondurables_sample_hp==1, 
family(poisson) link(log) corr(exch) vce(robust);
#delimit cr

margins, eydx(d_inflmedian)
margins, eydx(d_inflmedian) at(nomort = 0)
margins, eydx(d_inflmedian) at((p25) log_new_faminc (p25) log_new_faminc_bar)

**MARGINS FOR NEW TABLE

#delimit ;
xtgee nondurables_real c.d_inflmedian##i.nocoll c.d_infliqr##i.nocoll log_payments 
c.d_inflmedian##c.log_payments_bar c.d_inflmedian##c.nomort_bar c.d_inflmedian##c.log_new_faminc_bar 
i.unemp_increase i.unemp_decrease i.intrate_12m_up i.intrate_12m_down 
rw_expect d_wageiqr  log_new_faminc  i.nomort i.nocoll rage i.nonwhite i.female i.retired i.howner hppoint
IE_bar IE_unc_bar intrate_up_bar intrate_down_bar 
unemp_up_bar unemp_down_bar rw_bar rw_unc_bar howner_bar hp_bar 
i.spend_month [pweight=weight_samp] if nondurables_sample_hp==1, 
family(poisson) link(log) corr(exch) vce(robust);
#delimit cr

count if nondurables_sample_hp
local N1 = r(N)

local spec1 = " "
local spec2 = "at(nocoll = 1)"
local spec3 = "at(nocoll = 0)"
local spec4 = "at(nocoll = 1 (p25) log_new_faminc (p25) log_new_faminc_bar)"
local spec5 = "at(nocoll = 0 (p25) log_new_faminc (p25) log_new_faminc_bar)"
local spec6 = "at(nocoll = 1 (p75) log_new_faminc (p75) log_new_faminc_bar)"
local spec7 = "at(nocoll = 0 (p75) log_new_faminc (p75) log_new_faminc_bar)"


forval i = 1/7 {
	margins, eydx(d_inflmedian) `spec`i''
	mat table = r(table)
	local est`i' = table[1,1]
	loc se`i' = table[2,1]
	if table[4,1] <= .10 {
		local stars`i' = "\sym{*}"
	}
	if table[4,1] <= .05 {
		local stars`i' = "\sym{**}"
	}
	if table[4,1] <= .01 {
		local stars`i' = "\sym{***}"
	}
}


********************************************************************************
** MORTGAGOR SUBSAMPLE
********************************************************************************
** Running regression models with only mortgage holders.

keep if mort==1
drop if mort==1 & amtmort==. 
drop if howner==0 //Doesn't make sense that some people aren't homeowners AND have a mortgage...
** Log of mortgage balance 
gen log_amtmort = log(amtmort) if amtmort>0
replace log_amtmort=0 if amtmort==0

** Replacing log_mortgage amount with centered version
egen log_amtmort_sampmean=mean(log_amtmort)
replace log_amtmort=log_amtmort-log_amtmort_sampmean
** Generate within-household average log mortgage balance
egen log_amtmort_bar=mean(log_amtmort), by(id_new)
** Generate variable that equals 1 in all cases, to sum to determine observations per person
gen pre_obs3=1
** Generate sum of observations per person
egen obs4=total(pre_obs3), by(id_new)
sum obs4, d

** Define sample  based on sufficient observation
gen mort_samp=(obs4>=3)
** Label
la var log_amtmort_bar "Log Mean Mort. Balance"
la var log_amtmort "Log Mort. Balance"

**REPLICATE WORKING PAPER, Table 6b
#delimit ; 
xtgee nondurables_real c.d_inflmedian##i.nocoll c.d_infliqr##i.nocoll 
c.d_inflmedian##c.log_payments_bar c.d_inflmedian##c.log_amtmort_bar c.d_inflmedian##c.log_new_faminc_bar  
i.unemp_increase i.unemp_decrease i.intrate_12m_up i.intrate_12m_down log_amtmort log_payments log_new_faminc  
rw_expect d_wageiqr i.nocoll rage i.nonwhite i.female i.retired hppoint
IE_bar IE_unc_bar intrate_up_bar intrate_down_bar
unemp_up_bar unemp_down_bar 
rw_bar rw_unc_bar hp_bar
c.lag_IE##i.nocoll c.lag_infl_iqr##i.nocoll 
i.quarter [pweight=weight_samp] if mort_samp==1, 
family(poisson) link(log) corr(exch) vce(robust);
#delimit cr

margins, eydx(d_inflmedian)
margins, eydx(d_inflmedian) at(nocoll = 1)
margins, eydx(d_inflmedian) at(nocoll = 0)
margins, eydx(d_inflmedian) at(nocoll = 1 (p25) log_new_faminc (p25) log_new_faminc_bar)
margins, eydx(d_inflmedian) at(nocoll = 0 (p25) log_new_faminc (p25) log_new_faminc_bar)
margins, eydx(d_inflmedian) at(nocoll = 1 (p25) log_payments (p25) log_payments_bar)
margins, eydx(d_inflmedian) at(nocoll = 0 (p25) log_payments (p25) log_payments_bar)
margins, eydx(d_inflmedian) at(nocoll = 1 (p75) log_amtmort (p75) log_amtmort_bar)
margins, eydx(d_inflmedian) at(nocoll = 0 (p75) log_amtmort (p75) log_amtmort_bar)

**MARGINS FOR NEW TABLE

#delimit ; 
xtgee nondurables_real c.d_inflmedian##i.nocoll c.d_infliqr##i.nocoll 
c.d_inflmedian##c.log_payments_bar c.d_inflmedian##c.log_amtmort_bar c.d_inflmedian##c.log_new_faminc_bar  
i.unemp_increase i.unemp_decrease i.intrate_12m_up i.intrate_12m_down log_amtmort log_payments log_new_faminc  
rw_expect d_wageiqr i.nocoll rage i.nonwhite i.female i.retired hppoint
IE_bar IE_unc_bar intrate_up_bar intrate_down_bar
unemp_up_bar unemp_down_bar 
rw_bar rw_unc_bar hp_bar
i.spend_month [pweight=weight_samp] if mort_samp==1, 
family(poisson) link(log) corr(exch) vce(robust);
#delimit cr

local spec1 = " "
local spec2 = "at(nocoll = 1)"
local spec3 = "at(nocoll = 0)"
local spec4 = "at(nocoll = 1 (p25) log_new_faminc (p25) log_new_faminc_bar)"
local spec5 = "at(nocoll = 0 (p25) log_new_faminc (p25) log_new_faminc_bar)"
local spec6 = "at(nocoll = 1 (p75) log_new_faminc (p75) log_new_faminc_bar)"
local spec7 = "at(nocoll = 0 (p75) log_new_faminc (p75) log_new_faminc_bar)"
local spec8 = "at(nocoll = 1 (p25) log_amtmort (p25) log_amtmort_bar)"
local spec9 = "at(nocoll = 0 (p25) log_amtmort (p25) log_amtmort_bar)"
local spec10 = "at(nocoll = 1 (p75) log_amtmort (p75) log_amtmort_bar)"
local spec11 = "at(nocoll = 0 (p75) log_amtmort (p75) log_amtmort_bar)"

forval i = 1/11 {
	margins, eydx(d_inflmedian) `spec`i''
	mat table = r(table)
	local est`i'm = table[1,1]
	loc se`i'm = table[2,1]
	if table[4,1] <= .10 {
		local stars`i'm = "\sym{*}"
	}
	if table[4,1] <= .05 {
		local stars`i'm = "\sym{**}"
	}
	if table[4,1] <= .01 {
		local stars`i'm = "\sym{***}"
	}
}

count if mort_samp == 1
local N2 = r(N)

cap file close nondur 
file open nondur using "../Tables/margins_table_nondurables_temp1.tex", write replace

#delimit ;
file write nondur	"\begin{table}[h]" _n
					"\centering" _n
					"\def\sym#1{\ifmmode^{#1}\else\(^{#1}\)\fi}" _n
					"\caption{Average Marginal Effects of Inflation Expectations on Real Nondurables/Services Spending, As Semi-Elasticities}" _n
					"\begin{tabular}{lcc}" _n
					"\toprule" _n 
					"& \textbf{Baseline Sample} & \textbf{Mortgagor Sample} \\ " _n
					" & Model (6) & Model (7) \\" _n
					"\midrule" _n ;
#delimit cr	

local words = "Average No_College Some_College_or_More No_College,_25th_Percentile_Household_Income College,_25th_Percentile_Household_Income No_College,_75th_Percentile_Household_Income College,_75th_Percentile_Household_Income No_College,_25th_Percentile_Mortgage_Balance College,_25th_Percentile_Mortgage_Balance No_College,_75th_Percentile_Mortgage_Balance College,_75th_Percentile_Mortgage_Balance"

forval i = 1/7 {
	local label = word("`words'", `i')
	local label = subinstr("`label'", "_", " ", .)
	di "`label'"
	file write nondur " `label' & " %9.2f (`est`i'') "`stars`i'' & " %9.2f (`est`i'm') "`stars`i'm' \\" _n
	file write nondur	" & (" %9.2f (`se`i'') ") & (" %9.2f (`se`i'm') ") \\" _n
}

forval i = 8/11 {
	local label = word("`words'", `i')
	local label = subinstr("`label'", "_", " ", .)
	di "`label'"
	file write nondur " `label' &  & " %9.2f (`est`i'm') "`stars`i'm' \\" _n
	file write nondur	" &  & (" %9.2f (`se`i'm') ") \\" _n
}

file write nondur "\midrule" _n
file write nondur "Observations & `N1' & `N2' \\" _n
file write nondur "\bottomrule" _n
file write nondur "\end{tabular}" _n
file write nondur "\caption*{\doublespacing \normalsize Standard errors in parentheses \\" _n
file write nondur "\textit{Note:} A given semi-elasticity estimate indicates the population average relative change in monthly nondurable goods and services spending for a 1-percentage-point increase in expected inflation one year ahead, assuming the given characteristics. (Percentage changes are obtained by multiplying each value by 100.) \\ $\sym{*} p < 0.10, \sym{**} p <.05, \sym{***} p < .01$}" _n
file write nondur "\end{table}"
file close nondur

capture erase ../Tables/margins_table_nondurables.tex


* need to get rid of some extra spaces in parentheses
forval i = 1/5 {
filefilter ../Tables/margins_table_nondurables_temp`i'.tex ../Tables/margins_table_nondurables_temp`=`i'+1'.tex, from("( ") to("(")
erase ../Tables/margins_table_nondurables_temp`i'.tex
}

/* replace ! with mv on MacOS or Unix systems */
! rename "../Tables/margins_table_nondurables_temp6.tex" "../Tables/margins_table_nondurables.tex"

